Prediction of MRI Hardware Failures based on Image Features using Ensemble Learning
This work addresses the need for predictive maintenance in medical systems to prevent MRI hardware failures, though it is incremental as it applies existing ensemble methods to a specific domain.
The paper tackled the problem of predicting hardware failures in 20-channel Head/Neck MRI coils by classifying them as normal or broken, achieving an F-score of 94.14% and accuracy of 99.09% using an ensemble learning approach.
In order to ensure trouble-free operation, prediction of hardware failures is essential. This applies especially to medical systems. Our goal is to determine hardware which needs to be exchanged before failing. In this work, we focus on predicting failures of 20-channel Head/Neck coils using image-related measurements. Thus, we aim to solve a classification problem with two classes, normal and broken coil. To solve this problem, we use data of two different levels. One level refers to one-dimensional features per individual coil channel on which we found a fully connected neural network to perform best. The other data level uses matrices which represent the overall coil condition and feeds a different neural network. We stack the predictions of those two networks and train a Random Forest classifier as the ensemble learner. Thus, combining insights of both trained models improves the prediction results and allows us to determine the coil's condition with an F-score of 94.14% and an accuracy of 99.09%.